Overview

Dataset statistics

Number of variables23
Number of observations52
Missing cells441
Missing cells (%)36.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory186.5 B

Variable types

Numeric15
Unsupported7
Categorical1

Alerts

Year is highly overall correlated with Natural gas and 13 other fieldsHigh correlation
Natural gas is highly overall correlated with Year and 14 other fieldsHigh correlation
Coal is highly overall correlated with Year and 13 other fieldsHigh correlation
Hydroenergy is highly overall correlated with Year and 13 other fieldsHigh correlation
Firewood is highly overall correlated with Year and 13 other fieldsHigh correlation
Sugarcane and products is highly overall correlated with Year and 14 other fieldsHigh correlation
Other Primary_x000d_ is highly overall correlated with Year and 14 other fieldsHigh correlation
Total Primaries is highly overall correlated with Year and 13 other fieldsHigh correlation
Electricity is highly overall correlated with Year and 13 other fieldsHigh correlation
Diesel oil is highly overall correlated with Year and 13 other fieldsHigh correlation
Fuel oil is highly overall correlated with Natural gas and 4 other fieldsHigh correlation
Gases is highly overall correlated with Year and 13 other fieldsHigh correlation
Other secondary is highly overall correlated with Year and 14 other fieldsHigh correlation
Total Secundaries is highly overall correlated with Year and 13 other fieldsHigh correlation
Total is highly overall correlated with Year and 13 other fieldsHigh correlation
Charcoal is highly overall correlated with Year and 14 other fieldsHigh correlation
Oil has 52 (100.0%) missing valuesMissing
Natural gas has 20 (38.5%) missing valuesMissing
Nuclear has 52 (100.0%) missing valuesMissing
LPG has 52 (100.0%) missing valuesMissing
Gasoline/alcohol has 52 (100.0%) missing valuesMissing
Kerosene/jet fuel has 52 (100.0%) missing valuesMissing
Coke has 52 (100.0%) missing valuesMissing
Charcoal has 48 (92.3%) missing valuesMissing
Other secondary has 9 (17.3%) missing valuesMissing
Non-energy has 52 (100.0%) missing valuesMissing
Year is uniformly distributedUniform
Charcoal is uniformly distributedUniform
Year has unique valuesUnique
Other Primary_x000d_ has unique valuesUnique
Total Primaries has unique valuesUnique
Electricity has unique valuesUnique
Diesel oil has unique valuesUnique
Fuel oil has unique valuesUnique
Total Secundaries has unique valuesUnique
Total has unique valuesUnique
Oil is an unsupported type, check if it needs cleaning or further analysisUnsupported
Nuclear is an unsupported type, check if it needs cleaning or further analysisUnsupported
LPG is an unsupported type, check if it needs cleaning or further analysisUnsupported
Gasoline/alcohol is an unsupported type, check if it needs cleaning or further analysisUnsupported
Kerosene/jet fuel is an unsupported type, check if it needs cleaning or further analysisUnsupported
Coke is an unsupported type, check if it needs cleaning or further analysisUnsupported
Non-energy is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-07-30 07:36:52.114014
Analysis finished2023-07-30 07:37:52.984851
Duration1 minute and 0.87 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.5
Minimum1970
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:37:53.142862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972.55
Q11982.75
median1995.5
Q32008.25
95-th percentile2018.45
Maximum2021
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075944662
Kurtosis-1.2
Mean1995.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103766
Variance229.66667
MonotonicityStrictly increasing
2023-07-30T07:37:53.428632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 1
 
1.9%
1971 1
 
1.9%
1998 1
 
1.9%
1999 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1970 1
1.9%
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
ValueCountFrequency (%)
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%
2012 1
1.9%

Oil
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Natural gas
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing20
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean-1436.1081
Minimum-4527.85
Maximum-67.93
Zeros0
Zeros (%)0.0%
Negative32
Negative (%)61.5%
Memory size548.0 B
2023-07-30T07:37:53.679060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4527.85
5-th percentile-3537.6145
Q1-2403.9275
median-1076.35
Q3-279.75
95-th percentile-114.739
Maximum-67.93
Range4459.92
Interquartile range (IQR)2124.1775

Descriptive statistics

Standard deviation1258.1109
Coefficient of variation (CV)-0.87605583
Kurtosis-0.15468776
Mean-1436.1081
Median Absolute Deviation (MAD)928.325
Skewness-0.81838347
Sum-45955.46
Variance1582843
MonotonicityNot monotonic
2023-07-30T07:37:53.905867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
-1140.11 1
 
1.9%
-4527.85 1
 
1.9%
-2941.63 1
 
1.9%
-2845.5 1
 
1.9%
-2757.27 1
 
1.9%
-2656.49 1
 
1.9%
-2699.72 1
 
1.9%
-2575.1 1
 
1.9%
-2311.98 1
 
1.9%
-2346.87 1
 
1.9%
Other values (22) 22
42.3%
(Missing) 20
38.5%
ValueCountFrequency (%)
-4527.85 1
1.9%
-4266.04 1
1.9%
-2941.63 1
1.9%
-2845.5 1
1.9%
-2757.27 1
1.9%
-2699.72 1
1.9%
-2656.49 1
1.9%
-2575.1 1
1.9%
-2346.87 1
1.9%
-2325.54 1
1.9%
ValueCountFrequency (%)
-67.93 1
1.9%
-78.56 1
1.9%
-144.34 1
1.9%
-147.55 1
1.9%
-148.5 1
1.9%
-174.91 1
1.9%
-218.21 1
1.9%
-230.73 1
1.9%
-296.09 1
1.9%
-463.12 1
1.9%

Coal
Real number (ℝ)

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-81.06
Minimum-288.07
Maximum-9.05
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:54.149663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-288.07
5-th percentile-264.5105
Q1-109.3425
median-47.445
Q3-18.9675
95-th percentile-10.4115
Maximum-9.05
Range279.02
Interquartile range (IQR)90.375

Descriptive statistics

Standard deviation84.703408
Coefficient of variation (CV)-1.0449471
Kurtosis0.2575377
Mean-81.06
Median Absolute Deviation (MAD)32.225
Skewness-1.2542131
Sum-4215.12
Variance7174.6674
MonotonicityNot monotonic
2023-07-30T07:37:54.416237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13.83 2
 
3.8%
-9.59 1
 
1.9%
-42.89 1
 
1.9%
-76.27 1
 
1.9%
-62.82 1
 
1.9%
-65.02 1
 
1.9%
-48.65 1
 
1.9%
-35.79 1
 
1.9%
-46.14 1
 
1.9%
-52.67 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
-288.07 1
1.9%
-285.42 1
1.9%
-273.91 1
1.9%
-256.82 1
1.9%
-237.44 1
1.9%
-215.78 1
1.9%
-209.66 1
1.9%
-202.88 1
1.9%
-183.62 1
1.9%
-175.41 1
1.9%
ValueCountFrequency (%)
-9.05 1
1.9%
-9.59 1
1.9%
-10.12 1
1.9%
-10.65 1
1.9%
-11.18 1
1.9%
-11.56 1
1.9%
-11.96 1
1.9%
-12.25 1
1.9%
-13.83 2
3.8%
-16.61 1
1.9%

Hydroenergy
Real number (ℝ)

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-746.98885
Minimum-1948.39
Maximum-114.51
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:54.675226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1948.39
5-th percentile-1862.7565
Q1-1325.7575
median-339.995
Q3-240.0375
95-th percentile-131.5055
Maximum-114.51
Range1833.88
Interquartile range (IQR)1085.72

Descriptive statistics

Standard deviation646.90677
Coefficient of variation (CV)-0.86601932
Kurtosis-1.1922437
Mean-746.98885
Median Absolute Deviation (MAD)169.355
Skewness-0.7188987
Sum-38843.42
Variance418488.37
MonotonicityNot monotonic
2023-07-30T07:37:54.930353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-261.42 2
 
3.8%
-119.23 1
 
1.9%
-376.16 1
 
1.9%
-488.54 1
 
1.9%
-502.04 1
 
1.9%
-447.97 1
 
1.9%
-1008.07 1
 
1.9%
-1000.78 1
 
1.9%
-1047.71 1
 
1.9%
-1061.18 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
-1948.39 1
1.9%
-1897.54 1
1.9%
-1894.42 1
1.9%
-1836.85 1
1.9%
-1807.08 1
1.9%
-1774.97 1
1.9%
-1760.69 1
1.9%
-1666.62 1
1.9%
-1658.27 1
1.9%
-1657.23 1
1.9%
ValueCountFrequency (%)
-114.51 1
1.9%
-119.23 1
1.9%
-124.13 1
1.9%
-137.54 1
1.9%
-145.97 1
1.9%
-163.33 1
1.9%
-191.79 1
1.9%
-223.25 1
1.9%
-225.92 1
1.9%
-229.53 1
1.9%

Nuclear
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Firewood
Real number (ℝ)

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-159.25827
Minimum-387.2
Maximum-12.7
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:55.207517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-387.2
5-th percentile-368.7105
Q1-229.6775
median-136.985
Q3-80.65
95-th percentile-15.6915
Maximum-12.7
Range374.5
Interquartile range (IQR)149.0275

Descriptive statistics

Standard deviation113.14437
Coefficient of variation (CV)-0.71044579
Kurtosis-0.64940494
Mean-159.25827
Median Absolute Deviation (MAD)79.005
Skewness-0.58098771
Sum-8281.43
Variance12801.648
MonotonicityNot monotonic
2023-07-30T07:37:55.474716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-114.65 2
 
3.8%
-12.7 1
 
1.9%
-141.86 1
 
1.9%
-164.05 1
 
1.9%
-172.44 1
 
1.9%
-132.34 1
 
1.9%
-131.53 1
 
1.9%
-127.14 1
 
1.9%
-117.82 1
 
1.9%
-127.24 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
-387.2 1
1.9%
-383.67 1
1.9%
-373.38 1
1.9%
-364.89 1
1.9%
-353.48 1
1.9%
-347.58 1
1.9%
-333.34 1
1.9%
-309.93 1
1.9%
-307.95 1
1.9%
-294.11 1
1.9%
ValueCountFrequency (%)
-12.7 1
1.9%
-13.32 1
1.9%
-15.18 1
1.9%
-16.11 1
1.9%
-16.73 1
1.9%
-18.28 1
1.9%
-21.69 1
1.9%
-22 1
1.9%
-27.27 1
1.9%
-28.51 1
1.9%

Sugarcane and products
Real number (ℝ)

Distinct50
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1775.4169
Minimum-6570.86
Maximum-88.99
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:55.742230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6570.86
5-th percentile-6131.145
Q1-2224.2175
median-628.12
Q3-333.375
95-th percentile-109.135
Maximum-88.99
Range6481.87
Interquartile range (IQR)1890.8425

Descriptive statistics

Standard deviation2184.3351
Coefficient of variation (CV)-1.2303224
Kurtosis-0.10412283
Mean-1775.4169
Median Absolute Deviation (MAD)482.61
Skewness-1.2383219
Sum-92321.68
Variance4771319.9
MonotonicityNot monotonic
2023-07-30T07:37:56.004467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-106.44 2
 
3.8%
-111.34 2
 
3.8%
-2689.06 1
 
1.9%
-873.26 1
 
1.9%
-735.31 1
 
1.9%
-937.97 1
 
1.9%
-1075.5 1
 
1.9%
-1370.98 1
 
1.9%
-1406.46 1
 
1.9%
-1550.99 1
 
1.9%
Other values (40) 40
76.9%
ValueCountFrequency (%)
-6570.86 1
1.9%
-6240.7 1
1.9%
-6150.89 1
1.9%
-6114.99 1
1.9%
-6076.45 1
1.9%
-5964.74 1
1.9%
-5782.78 1
1.9%
-5677.79 1
1.9%
-5270.09 1
1.9%
-4431.47 1
1.9%
ValueCountFrequency (%)
-88.99 1
1.9%
-106.44 2
3.8%
-111.34 2
3.8%
-115.81 1
1.9%
-123.05 1
1.9%
-167.97 1
1.9%
-186.28 1
1.9%
-208.42 1
1.9%
-218.85 1
1.9%
-238.86 1
1.9%

Other Primary_x000d_
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1475.4065
Minimum-5688.41
Maximum-79.87
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:56.284431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5688.41
5-th percentile-4516.4535
Q1-2168.135
median-786.375
Q3-329.8225
95-th percentile-91.123
Maximum-79.87
Range5608.54
Interquartile range (IQR)1838.3125

Descriptive statistics

Standard deviation1512.2697
Coefficient of variation (CV)-1.0249851
Kurtosis0.44828478
Mean-1475.4065
Median Absolute Deviation (MAD)685.135
Skewness-1.2008412
Sum-76721.14
Variance2286959.6
MonotonicityNot monotonic
2023-07-30T07:37:56.527571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-81.16 1
 
1.9%
-79.87 1
 
1.9%
-917.86 1
 
1.9%
-1081.34 1
 
1.9%
-1439.07 1
 
1.9%
-1566.21 1
 
1.9%
-1683.26 1
 
1.9%
-1731.85 1
 
1.9%
-1827.21 1
 
1.9%
-2166.63 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-5688.41 1
1.9%
-4917.23 1
1.9%
-4601.06 1
1.9%
-4447.23 1
1.9%
-4294.23 1
1.9%
-4137.63 1
1.9%
-3884.28 1
1.9%
-3710.9 1
1.9%
-2895.59 1
1.9%
-2615.38 1
1.9%
ValueCountFrequency (%)
-79.87 1
1.9%
-81.16 1
1.9%
-88.45 1
1.9%
-93.31 1
1.9%
-94.16 1
1.9%
-95.02 1
1.9%
-107.46 1
1.9%
-107.6 1
1.9%
-119.9 1
1.9%
-185.06 1
1.9%

Total Primaries
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5121.8902
Minimum-18135.05
Maximum-311.67
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:56.777266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-18135.05
5-th percentile-15894.821
Q1-7297.07
median-2161.925
Q3-1031.6625
95-th percentile-357.76
Maximum-311.67
Range17823.38
Interquartile range (IQR)6265.4075

Descriptive statistics

Standard deviation5623.6329
Coefficient of variation (CV)-1.0979605
Kurtosis-0.19701289
Mean-5121.8902
Median Absolute Deviation (MAD)1733.125
Skewness-1.1212061
Sum-266338.29
Variance31625247
MonotonicityNot monotonic
2023-07-30T07:37:57.031473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-311.67 1
 
1.9%
-326.39 1
 
1.9%
-2649.95 1
 
1.9%
-3146.59 1
 
1.9%
-3476.54 1
 
1.9%
-3854.83 1
 
1.9%
-4709.19 1
 
1.9%
-5145.95 1
 
1.9%
-5515.28 1
 
1.9%
-6116.88 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-18135.05 1
1.9%
-17699.18 1
1.9%
-15949.15 1
1.9%
-15850.37 1
1.9%
-15401.83 1
1.9%
-15353.21 1
1.9%
-14946.73 1
1.9%
-14369.04 1
1.9%
-12803.3 1
1.9%
-11573.34 1
1.9%
ValueCountFrequency (%)
-311.67 1
1.9%
-326.39 1
1.9%
-356 1
1.9%
-359.2 1
1.9%
-379.18 1
1.9%
-407.03 1
1.9%
-450.57 1
1.9%
-534.65 1
1.9%
-586.45 1
1.9%
-678.91 1
1.9%

Electricity
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3039.7402
Minimum318.85
Maximum9776.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:37:57.311265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum318.85
5-th percentile364.6075
Q1785.9
median1412.715
Q34510.8875
95-th percentile8725.717
Maximum9776.97
Range9458.12
Interquartile range (IQR)3724.9875

Descriptive statistics

Standard deviation3052.0675
Coefficient of variation (CV)1.0040554
Kurtosis-0.39916066
Mean3039.7402
Median Absolute Deviation (MAD)958.43
Skewness1.0459479
Sum158066.49
Variance9315116.2
MonotonicityNot monotonic
2023-07-30T07:37:57.573490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
318.85 1
 
1.9%
322.54 1
 
1.9%
1765.29 1
 
1.9%
2066.18 1
 
1.9%
2150 1
 
1.9%
2337.49 1
 
1.9%
2921.99 1
 
1.9%
3058.27 1
 
1.9%
3252.35 1
 
1.9%
3406.94 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
318.85 1
1.9%
322.54 1
1.9%
349.62 1
1.9%
376.87 1
1.9%
394.67 1
1.9%
435.67 1
1.9%
472.9 1
1.9%
509.6 1
1.9%
584.48 1
1.9%
674.66 1
1.9%
ValueCountFrequency (%)
9776.97 1
1.9%
9776.54 1
1.9%
8783.28 1
1.9%
8678.62 1
1.9%
8453.22 1
1.9%
8422.51 1
1.9%
8311.41 1
1.9%
8085.36 1
1.9%
7413.7 1
1.9%
6713.85 1
1.9%

LPG
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Gasoline/alcohol
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Kerosene/jet fuel
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Diesel oil
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-199.255
Minimum-574.53
Maximum-15.43
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:57.820420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-574.53
5-th percentile-444.8685
Q1-307.2875
median-136.95
Q3-84.455
95-th percentile-22.547
Maximum-15.43
Range559.1
Interquartile range (IQR)222.8325

Descriptive statistics

Standard deviation146.90744
Coefficient of variation (CV)-0.73728359
Kurtosis-0.22687325
Mean-199.255
Median Absolute Deviation (MAD)76.08
Skewness-0.81438961
Sum-10361.26
Variance21581.796
MonotonicityNot monotonic
2023-07-30T07:37:58.080590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-77.16 1
 
1.9%
-57.44 1
 
1.9%
-239.42 1
 
1.9%
-328.58 1
 
1.9%
-364.04 1
 
1.9%
-501.15 1
 
1.9%
-216.66 1
 
1.9%
-155.73 1
 
1.9%
-160.73 1
 
1.9%
-168.42 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-574.53 1
1.9%
-561.98 1
1.9%
-501.15 1
1.9%
-398.82 1
1.9%
-390.39 1
1.9%
-381.46 1
1.9%
-380.85 1
1.9%
-378.37 1
1.9%
-370.34 1
1.9%
-364.04 1
1.9%
ValueCountFrequency (%)
-15.43 1
1.9%
-16.29 1
1.9%
-19.72 1
1.9%
-24.86 1
1.9%
-57.44 1
1.9%
-64.3 1
1.9%
-67.7 1
1.9%
-68.59 1
1.9%
-71.16 1
1.9%
-72.49 1
1.9%

Fuel oil
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-338.79942
Minimum-521.58
Maximum-201.14
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:58.342858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-521.58
5-th percentile-447.1705
Q1-396.0325
median-340.815
Q3-266.63
95-th percentile-227.025
Maximum-201.14
Range320.44
Interquartile range (IQR)129.4025

Descriptive statistics

Standard deviation78.609409
Coefficient of variation (CV)-0.23202344
Kurtosis-0.65850716
Mean-338.79942
Median Absolute Deviation (MAD)60.98
Skewness-0.18404824
Sum-17617.57
Variance6179.4392
MonotonicityNot monotonic
2023-07-30T07:37:58.593033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-329.53 1
 
1.9%
-245.76 1
 
1.9%
-456.13 1
 
1.9%
-413.45 1
 
1.9%
-362.22 1
 
1.9%
-395.15 1
 
1.9%
-373.8 1
 
1.9%
-305.84 1
 
1.9%
-295.96 1
 
1.9%
-298.37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-521.58 1
1.9%
-512.28 1
1.9%
-456.13 1
1.9%
-439.84 1
1.9%
-439.13 1
1.9%
-437 1
1.9%
-422.79 1
1.9%
-417.59 1
1.9%
-416.3 1
1.9%
-413.45 1
1.9%
ValueCountFrequency (%)
-201.14 1
1.9%
-203.6 1
1.9%
-225.32 1
1.9%
-228.42 1
1.9%
-235.65 1
1.9%
-238.18 1
1.9%
-245.76 1
1.9%
-251.25 1
1.9%
-251.81 1
1.9%
-253 1
1.9%

Coke
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Charcoal
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct4
Distinct (%)100.0%
Missing48
Missing (%)92.3%
Memory size548.0 B
-15.4
-15.16
-8.81
-24.41

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters22
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row-15.4
2nd row-15.16
3rd row-8.81
4th row-24.41

Common Values

ValueCountFrequency (%)
-15.4 1
 
1.9%
-15.16 1
 
1.9%
-8.81 1
 
1.9%
-24.41 1
 
1.9%
(Missing) 48
92.3%

Length

2023-07-30T07:37:58.844688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:37:59.091195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15.4 1
25.0%
15.16 1
25.0%
8.81 1
25.0%
24.41 1
25.0%

Most occurring characters

ValueCountFrequency (%)
1 5
22.7%
- 4
18.2%
. 4
18.2%
4 3
13.6%
5 2
 
9.1%
8 2
 
9.1%
6 1
 
4.5%
2 1
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14
63.6%
Dash Punctuation 4
 
18.2%
Other Punctuation 4
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
35.7%
4 3
21.4%
5 2
 
14.3%
8 2
 
14.3%
6 1
 
7.1%
2 1
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
22.7%
- 4
18.2%
. 4
18.2%
4 3
13.6%
5 2
 
9.1%
8 2
 
9.1%
6 1
 
4.5%
2 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
22.7%
- 4
18.2%
. 4
18.2%
4 3
13.6%
5 2
 
9.1%
8 2
 
9.1%
6 1
 
4.5%
2 1
 
4.5%

Gases
Real number (ℝ)

Distinct50
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-159.26192
Minimum-553.45
Maximum-9.46
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:37:59.326412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-553.45
5-th percentile-369.983
Q1-264.86
median-117.77
Q3-58.675
95-th percentile-9.933
Maximum-9.46
Range543.99
Interquartile range (IQR)206.185

Descriptive statistics

Standard deviation129.08491
Coefficient of variation (CV)-0.81051958
Kurtosis0.21227913
Mean-159.26192
Median Absolute Deviation (MAD)75.43
Skewness-0.90925872
Sum-8281.62
Variance16662.913
MonotonicityNot monotonic
2023-07-30T07:37:59.585707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.46 3
 
5.8%
-335.92 1
 
1.9%
-185.26 1
 
1.9%
-167.63 1
 
1.9%
-180.53 1
 
1.9%
-188.26 1
 
1.9%
-131.53 1
 
1.9%
-141.47 1
 
1.9%
-109.77 1
 
1.9%
-139.56 1
 
1.9%
Other values (40) 40
76.9%
ValueCountFrequency (%)
-553.45 1
1.9%
-394.47 1
1.9%
-381.17 1
1.9%
-360.83 1
1.9%
-349.33 1
1.9%
-349.19 1
1.9%
-335.92 1
1.9%
-334.39 1
1.9%
-315.65 1
1.9%
-311.33 1
1.9%
ValueCountFrequency (%)
-9.46 3
5.8%
-10.32 1
 
1.9%
-10.75 1
 
1.9%
-12.47 1
 
1.9%
-27.08 1
 
1.9%
-30.95 1
 
1.9%
-32.24 1
 
1.9%
-36.11 1
 
1.9%
-37.4 1
 
1.9%
-47.71 1
 
1.9%

Other secondary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)97.7%
Missing9
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean-294.6014
Minimum-747.32
Maximum-0.88
Zeros0
Zeros (%)0.0%
Negative43
Negative (%)82.7%
Memory size548.0 B
2023-07-30T07:37:59.830994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-747.32
5-th percentile-605.945
Q1-492.7
median-310.32
Q3-52.495
95-th percentile-8.06
Maximum-0.88
Range746.44
Interquartile range (IQR)440.205

Descriptive statistics

Standard deviation217.69612
Coefficient of variation (CV)-0.73895141
Kurtosis-1.1532206
Mean-294.6014
Median Absolute Deviation (MAD)230.88
Skewness-0.13345524
Sum-12667.86
Variance47391.599
MonotonicityNot monotonic
2023-07-30T07:38:00.074043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
-12.37 2
 
3.8%
-440.62 1
 
1.9%
-310.32 1
 
1.9%
-372.79 1
 
1.9%
-264.04 1
 
1.9%
-335.36 1
 
1.9%
-346.66 1
 
1.9%
-572.78 1
 
1.9%
-366.33 1
 
1.9%
-465.4 1
 
1.9%
Other values (32) 32
61.5%
(Missing) 9
 
17.3%
ValueCountFrequency (%)
-747.32 1
1.9%
-623.14 1
1.9%
-609.21 1
1.9%
-576.56 1
1.9%
-572.78 1
1.9%
-571.18 1
1.9%
-555.25 1
1.9%
-551.3 1
1.9%
-541.2 1
1.9%
-528.73 1
1.9%
ValueCountFrequency (%)
-0.88 1
1.9%
-2.63 1
1.9%
-7.97 1
1.9%
-8.87 1
1.9%
-12.37 2
3.8%
-29.54 1
1.9%
-30.89 1
1.9%
-48.7 1
1.9%
-49.53 1
1.9%
-52.09 1
1.9%

Non-energy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Total Secundaries
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3039.7402
Minimum318.85
Maximum9776.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:38:00.346425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum318.85
5-th percentile364.6075
Q1785.9
median1412.715
Q34510.8875
95-th percentile8725.717
Maximum9776.97
Range9458.12
Interquartile range (IQR)3724.9875

Descriptive statistics

Standard deviation3052.0675
Coefficient of variation (CV)1.0040554
Kurtosis-0.39916066
Mean3039.7402
Median Absolute Deviation (MAD)958.43
Skewness1.0459479
Sum158066.49
Variance9315116.2
MonotonicityNot monotonic
2023-07-30T07:38:00.607910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
318.85 1
 
1.9%
322.54 1
 
1.9%
1765.29 1
 
1.9%
2066.18 1
 
1.9%
2150 1
 
1.9%
2337.49 1
 
1.9%
2921.99 1
 
1.9%
3058.27 1
 
1.9%
3252.35 1
 
1.9%
3406.94 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
318.85 1
1.9%
322.54 1
1.9%
349.62 1
1.9%
376.87 1
1.9%
394.67 1
1.9%
435.67 1
1.9%
472.9 1
1.9%
509.6 1
1.9%
584.48 1
1.9%
674.66 1
1.9%
ValueCountFrequency (%)
9776.97 1
1.9%
9776.54 1
1.9%
8783.28 1
1.9%
8678.62 1
1.9%
8453.22 1
1.9%
8422.51 1
1.9%
8311.41 1
1.9%
8085.36 1
1.9%
7413.7 1
1.9%
6713.85 1
1.9%

Total
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3024.3035
Minimum-10042.21
Maximum-316.51
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)100.0%
Memory size548.0 B
2023-07-30T07:38:00.854042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-10042.21
5-th percentile-8582.494
Q1-4387.175
median-1665.775
Q3-758.4775
95-th percentile-366.1815
Maximum-316.51
Range9725.7
Interquartile range (IQR)3628.6975

Descriptive statistics

Standard deviation2964.1207
Coefficient of variation (CV)-0.9801003
Kurtosis-0.18881415
Mean-3024.3035
Median Absolute Deviation (MAD)1258.115
Skewness-1.085069
Sum-157263.78
Variance8786011.8
MonotonicityNot monotonic
2023-07-30T07:38:01.112646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-408.96 1
 
1.9%
-316.51 1
 
1.9%
-1958.29 1
 
1.9%
-2318.31 1
 
1.9%
-2565.57 1
 
1.9%
-2974.54 1
 
1.9%
-2927.03 1
 
1.9%
-2991.09 1
 
1.9%
-3233.88 1
 
1.9%
-3565.93 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-10042.21 1
1.9%
-9655.28 1
1.9%
-8619.3 1
1.9%
-8552.38 1
1.9%
-8499.52 1
1.9%
-8297.02 1
1.9%
-8215.07 1
1.9%
-7872.39 1
1.9%
-6965.01 1
1.9%
-6387.19 1
1.9%
ValueCountFrequency (%)
-316.51 1
1.9%
-344.31 1
1.9%
-356.21 1
1.9%
-374.34 1
1.9%
-403.26 1
1.9%
-406.36 1
1.9%
-408.96 1
1.9%
-461.44 1
1.9%
-490.93 1
1.9%
-587.68 1
1.9%

Interactions

2023-07-30T07:37:43.263959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:52.649902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:58.628058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:01.621385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:04.746546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:08.057914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:12.514891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:15.759032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:18.855250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:22.137913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:26.526167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:29.716278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:32.843059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:35.997442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:40.142757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:43.675078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:53.124716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:59.056976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:02.042701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:05.166056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:08.478899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:12.950449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:16.162637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:19.289708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:22.631701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:26.915221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:30.132643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:33.253360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:36.522235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:40.537945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:43.846518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:53.407183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:59.219823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:02.209734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:05.350599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:08.647006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:13.135460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:16.330486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:19.462542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:22.895735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:27.093570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:30.303756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:33.432640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:36.803881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:40.706956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:44.058943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:53.739382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:59.394711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:02.395890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:05.555264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:08.864525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:13.341052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:16.512925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:19.669990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:23.222934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:27.305907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:30.510325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:33.623943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:37.037914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:40.918436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:48.309500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:54.071975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:59.611439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:02.613282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:05.774642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:09.079873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:13.561902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:16.740080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:19.887762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:23.552844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:27.521165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:30.706922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:33.821027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:37.312440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:41.134958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:48.517509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:54.419189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:59.789089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:02.827868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:05.994129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:09.335999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:13.773073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:16.945075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:20.111958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:23.892078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:27.743121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:30.913109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:34.024152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:37.650502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:41.338516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:48.722257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:54.750408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:59.980575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:03.022077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:06.223040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:09.664985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:13.977337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:17.147031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:20.329021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:24.214418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:27.945759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:31.108676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:34.213536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:37.987737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:41.536039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:48.901551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:55.073900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:00.152797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:03.210766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:06.413042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:09.983447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:14.167102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:17.323912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:20.519680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:24.528012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:28.122055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:31.294512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:34.386440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:38.256467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:41.716272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:49.140893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:55.422505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:00.353101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:03.426172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:06.634321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:10.323596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:14.391574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:17.535634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:20.726708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:24.863909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:28.328575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:31.507082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:34.590733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:38.608421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:41.948257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:49.393937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:55.745101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:00.532908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:03.602165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:06.830858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:10.639952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:14.585862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:17.716857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:20.929948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:25.190238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:28.535414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:31.717427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:34.767310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:38.931584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:42.128448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:49.710114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:56.191662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:00.716733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:03.794341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:07.034838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:10.973565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:14.777982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:17.902081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:21.123125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:25.501618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:28.716920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:31.909302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:34.943600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:39.185067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:42.318188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:50.035774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:56.715585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:00.896329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:03.995275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:07.236383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:11.313783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:14.986545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:18.111920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:21.333227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:25.747236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:28.915433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:32.093591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:35.149031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:39.385249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:42.510909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:50.354909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:57.224051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:01.064671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:04.166342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:07.432054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:11.635248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:15.187232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:18.291294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:21.524131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:25.919852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:29.113448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:32.267437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:35.308960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:39.561537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:42.691413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:50.670056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:57.709170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:01.263313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:04.372833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:07.641199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:11.965890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:15.391921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:18.490094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:21.748395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:26.147407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:29.304490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:32.456710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:35.499150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:39.770115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:42.897129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:50.978936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:36:58.177725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:01.432749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:04.550841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:07.844508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:12.303092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:15.579248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:18.675105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:21.935418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:26.342388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:29.508858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:32.667811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:35.735671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:39.958312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:37:43.088946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-30T07:38:01.347261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearNatural gasCoalHydroenergyFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityDiesel oilFuel oilGasesOther secondaryTotal SecundariesTotalCharcoal
Year1.000-0.989-0.924-0.962-0.935-0.994-0.994-0.9990.999-0.9300.498-0.939-0.9480.999-0.9961.000
Natural gas-0.9891.0000.7330.8760.7280.9780.9740.988-0.9880.753-0.9070.8080.886-0.9880.9841.000
Coal-0.9240.7331.0000.8700.9490.9190.9190.923-0.9260.863-0.3920.8690.850-0.9260.9231.000
Hydroenergy-0.9620.8760.8701.0000.8760.9630.9540.961-0.9620.904-0.4740.9070.919-0.9620.9561.000
Firewood-0.9350.7280.9490.8761.0000.9240.9300.934-0.9350.886-0.4170.9220.839-0.9350.9331.000
Sugarcane and products-0.9940.9780.9190.9630.9241.0000.9880.994-0.9950.922-0.5010.9250.944-0.9950.9901.000
Other Primary_x000d_\n-0.9940.9740.9190.9540.9300.9881.0000.997-0.9940.928-0.5020.9420.944-0.9940.9971.000
Total Primaries-0.9990.9880.9230.9610.9340.9940.9971.000-0.9990.928-0.4990.9380.947-0.9990.9971.000
Electricity0.999-0.988-0.926-0.962-0.935-0.995-0.994-0.9991.000-0.9280.495-0.935-0.9481.000-0.9951.000
Diesel oil-0.9300.7530.8630.9040.8860.9220.9280.928-0.9281.000-0.4340.8910.904-0.9280.9321.000
Fuel oil0.498-0.907-0.392-0.474-0.417-0.501-0.502-0.4990.495-0.4341.000-0.479-0.7010.495-0.4941.000
Gases-0.9390.8080.8690.9070.9220.9250.9420.938-0.9350.891-0.4791.0000.876-0.9350.9421.000
Other secondary-0.9480.8860.8500.9190.8390.9440.9440.947-0.9480.904-0.7010.8761.000-0.9480.9521.000
Total Secundaries0.999-0.988-0.926-0.962-0.935-0.995-0.994-0.9991.000-0.9280.495-0.935-0.9481.000-0.9951.000
Total-0.9960.9840.9230.9560.9330.9900.9970.997-0.9950.932-0.4940.9420.952-0.9951.0001.000
Charcoal1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-07-30T07:37:51.515410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T07:37:52.482547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T07:37:52.824672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

451YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
11970NaNNaN-9.59-119.23NaN-12.70-88.99-81.16-311.67318.85NaNNaNNaN-77.16-329.53NaNNaN-9.46NaNNaN318.85-408.96
21971NaNNaN-12.25-114.51NaN-13.32-106.44-79.87-326.39322.54NaNNaNNaN-57.44-245.76NaNNaN-9.46NaNNaN322.54-316.51
31972NaNNaN-11.18-124.13NaN-15.18-111.34-94.16-356.00349.62NaNNaNNaN-64.30-264.17NaNNaN-9.46NaNNaN349.62-344.31
41973NaNNaN-10.65-137.54NaN-16.11-106.44-88.45-359.20376.87NaNNaNNaN-68.59-294.55NaNNaN-10.75NaNNaN376.87-356.21
51974NaNNaN-10.12-145.97NaN-16.73-111.34-95.02-379.18394.67NaNNaNNaN-71.16-308.36NaNNaN-10.32NaNNaN394.67-374.34
61975NaNNaN-9.05-163.33NaN-18.28-123.05-93.31-407.03435.67NaNNaNNaN-78.87-340.57NaNNaN-12.47NaNNaN435.67-403.26
71976NaNNaN-13.83-191.79NaN-21.69-115.81-107.46-450.57472.90NaNNaNNaN-16.29-385.31NaNNaN-27.08NaNNaN472.90-406.36
81977NaNNaN-13.83-223.25NaN-22.00-167.97-107.60-534.65509.60NaNNaNNaN-15.43-390.01NaNNaN-30.95NaNNaN509.60-461.44
91978NaNNaN-16.61-236.41NaN-27.27-186.28-119.90-586.45584.48NaNNaNNaN-19.72-437.00NaNNaN-32.24NaNNaN584.48-490.93
101979NaNNaN-20.58-225.92NaN-28.51-218.85-185.06-678.91674.66NaNNaNNaN-24.86-521.58NaNNaN-36.11-0.88NaN674.66-587.68
451YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
432012NaN-2346.87-161.11-1760.69NaN-257.81-4431.47-2615.38-11573.346713.85NaNNaNNaN-390.39-286.40NaNNaN-274.35-576.56NaN6713.85-6387.19
442013NaN-2311.98-158.16-1897.54NaN-269.94-5270.09-2895.59-12803.307413.70NaNNaNNaN-300.19-264.59NaNNaN-263.30-747.32NaN7413.70-6965.01
452014NaN-2575.10-202.88-1894.42NaN-307.95-5677.79-3710.90-14369.048085.36NaNNaNNaN-378.37-251.81NaNNaN-349.33-609.21NaN8085.36-7872.39
462015NaN-2699.72-237.44-1807.08NaN-353.48-5964.74-3884.28-14946.738311.41NaNNaNNaN-398.82-251.25NaNNaN-306.54-623.14NaN8311.41-8215.07
472016NaN-2656.49-273.91-1836.85NaN-333.34-6114.99-4137.63-15353.218453.22NaNNaNNaN-370.34-228.42NaNNaN-269.54-528.73NaN8453.22-8297.02
482017NaN-2757.27-256.82-1595.03NaN-347.58-6150.89-4294.23-15401.838422.51NaNNaNNaN-380.85-238.18NaNNaN-381.17-520.00NaN8422.51-8499.52
492018NaN-2845.50-285.42-1658.27NaN-383.67-6076.45-4601.06-15850.378678.62NaNNaNNaN-292.53-253.00NaNNaN-360.83-541.20NaN8678.62-8619.30
502019NaN-2941.63-288.07-1666.62NaN-364.89-6240.70-4447.23-15949.158783.28NaNNaNNaN-280.93-201.14NaNNaN-349.19-555.25NaN8783.28-8552.38
512020NaN-4527.85-209.66-1522.25NaN-387.20-6570.86-4917.23-18135.059776.54NaNNaNNaN-574.53-203.60NaNNaN-334.39-571.18NaN9776.54-10042.21
522021NaN-4266.04-215.78-1372.79NaN-373.38-5782.78-5688.41-17699.189776.97NaNNaNNaN-561.98-225.32NaNNaN-394.47-551.30NaN9776.97-9655.28